International market selection: an application of hybrid multi-criteria decision-making technique in the textile sector
Bibliographic record
Abstract
Purpose The selection of an international market (IMS) is a prime factor in the success and growth of a company. Therefore, the purpose of this study is to consolidate and apply a systematic methodology that contributes toward the evaluation of international markets and promotes entry into the export market of Antioquia’s textile companies. Design/methodology/approach Through a systematic literature review, the criteria and sub-criteria involved in the IMS process are identified and a total of 5 general criteria and 23 sub-criteria are selected. A hybrid approach is used to address the gap. In total, a multiple case study of 11 companies from different range of export values are selected. Data analysis is conducted using two multiple criteria decision-making (MCDM) models, namely, the analytic hierarchy process for weighting the factors and the technique for order of preference by similarity to the ideal solution for the country selection ranking. Findings The results demonstrate the applicability of the hybrid MCDM technique to improve IMS decision-making in the textile sector and other sectors. It is found that Canada, Belgium and the UK are the best destinations for textile exports with a selection score of 0.7716, 0.7488 and 0.7337, respectively. The sub-criteria belonging to the dimensions of trade barriers, economic factors and costs are the main factors affecting the export of a textile-clothing product. Research limitations/implications The possibility of achieving a generalized result through this case study is not possible, but the methodological application carried out is a novel for the selection of markets in the Colombian case and within the literature available in the domain. Practical implications From the managerial point of view, firms associated with trade have a broader vision when looking for new markets. Emerging entrepreneurs can equip themselves to enter the international market. Practitioners and policymakers can also use this methodology, which will allow them to evaluate new markets to outline promotional strategies for positioning products abroad. Social implications To facilitate the selection of international markets for enterprises. Originality/value The contribution of the study is twofold. First, the combination of techniques will allow wider support for the selection of markets and act as a decision support system. On the other hand, this is the first time that such a methodology is used for IMS in the exporting sector not only in Colombia but also in Latin America. Finally, the detailed methodological process described in the study allows both academicians and decision-makers to replicate the study in other contexts and scenarios.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.031 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.017 | 0.011 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".